HeLo: Heterogeneous Multi-Modal Fusion with Label Correlation for Emotion Distribution Learning

Multi-modal emotion recognition has garnered increasing attention as it plays a significant role in human-computer interaction (HCI) in recent years. Since different discrete emotions may exist at the same time, compared with single-class emotion recognition, emotion distribution learning (EDL) that identifies a mixture of basic emotions has gradually emerged as a trend. However, existing EDL methods face challenges in mining the heterogeneity among multiple modalities. Besides, rich semantic correlations across arbitrary basic emotions are not fully exploited. In this paper, we propose a multi-modal emotion distribution learning framework, named HeLo, aimed at fully exploring the heterogeneity and complementary information in multi-modal emotional data and label correlation within mixed basic emotions. Specifically, we first adopt cross-attention to effectively fuse the physiological data. Then, an optimal transport (OT)-based heterogeneity mining module is devised to mine the interaction and heterogeneity between the physiological and behavioral representations. To facilitate label correlation learning, we introduce a learnable label embedding optimized by correlation matrix alignment. Finally, the learnable label embeddings and label correlation matrices are integrated with the multi-modal representations through a novel label correlation-driven cross-attention mechanism for accurate emotion distribution learning. Experimental results on two publicly available datasets demonstrate the superiority of our proposed method in emotion distribution learning.

ViEEG: Hierarchical Neural Coding with Cross-Modal Progressive Enhancement for EEG-Based Visual Decoding

Understanding and decoding brain activity into visual representations is a fundamental challenge at the intersection of neuroscience and artificial intelligence. While EEG-based visual decoding has shown promise due to its non-invasive, low-cost nature and millisecond-level temporal resolution, existing methods are limited by their reliance on flat neural representations that overlook the brain's inherent visual hierarchy. In this paper, we introduce ViEEG, a biologically inspired hierarchical EEG decoding framework that aligns with the Hubel-Wiesel theory of visual processing. ViEEG decomposes each visual stimulus into three biologically aligned components-contour, foreground object, and contextual scene-serving as anchors for a three-stream EEG encoder. These EEG features are progressively integrated via cross-attention routing, simulating cortical information flow from V1 to IT to the association cortex. We further adopt hierarchical contrastive learning to align EEG representations with CLIP embeddings, enabling zero-shot object recognition. Extensive experiments on the THINGS-EEG dataset demonstrate that ViEEG achieves state-of-the-art performance, with 40.9% Top-1 accuracy in subject-dependent and 22.9% Top-1 accuracy in cross-subject settings, surpassing existing methods by over 45%. Our framework not only advances the performance frontier but also sets a new paradigm for biologically grounded brain decoding in AI.